Method for generating a category clustering data using a data transmission structure

ABSTRACT

A method for generating a category clustering data via a code division multiple access (CDMA) structure comprises the steps of: dividing a dataset to generate dataset categories; and generating the category clustering data via the CDMA structure processing the dataset categories according to the dataset categories; wherein the dataset includes a plurality of variable sequences; wherein dividing the dataset includes the step of: using a variable slope of each of the variable sequences to perform a segment division on a corresponding variable sequence to generate a plurality of segments; and using a distance, an angle and a slope to perform an affinity group on the variable sequences to generate a plurality of groups. The method for generating the category clustering data via the CDMA structure can make the category clustering data to have a very high similarity.

FIELD OF THE INVENTION

The present invention relates to a method for generating a categoryclustering data; in particular, to a data transmission structure basedmethod for generating a category clustering data

BACKGROUND OF THE INVENTION

Time length is not applicable as a distance in traditional fuzzy C-means(FCM) algorithms, and information of a distance change in a distanceaxis can only be known, a lack of smoothness of a shape change cannot besolved because the distance lacks information of time, and a goodcorrect rate of category clustering cannot be provided because a shapevariation is decided by a slope. Traditional fuzzy slope time series(FSTS) algorithms are not adaptable for unstable wave motions andinstant change in angle, information of a relative change of a similarshape trend in a time axis can only be known, rapid fluctuations in along time sequence of an economic time or a cycle swing of a trend curvecannot be solved because of an insufficient information of the trendcurve, and correct rate of category clustering cannot be providedbecause a wave motion variation is decided by an angle. Traditionalfuzzy spectral angle matching (F SAM) algorithms are not adaptable forlength change of the distance, a relative polarity of three axis betweenvariables and an origin, and a change in a relationship between astability and a wave motion angle can only be known, a distancevariation of fast moving, long distance, short distance in distancecannot be provided because of an insufficient information of thedistance caused by drastic changes of the trend curve, and correct rateof category clustering cannot be provided because variation of thedistance is decided by the distance.

Traditional data transmitting and receiving are carried out directly bytransmission lines, rarely by channel modules. Even if the channelmodules are used, only hardware structure processing is involved. Theyhave nothing to do with analyzing category clustering data, and aprocessing method for analyzing category clustering data and combine anideal combination channel of variables cannot be found.

SUMMARY OF THE INVENTION

An exemplary embodiment of the instant disclosure provides a method forgenerating a category clustering data via a data transmission structure.In detail, the exemplary embodiment of the instant disclosure provides amethod for generating a category clustering data via a code divisionmultiple access (CDMA) structure comprising steps of: dividing a datasetto generate dataset categories; and according to the divided datasetcategories and, by processing the dataset categories via the CDMAstructure, to generate the category clustering data; wherein the datasetincludes a plurality of variable sequences; wherein dividing the datasetincludes steps of: using a variable slope of each of the variablesequences to perform a segment division on a corresponding variablesequence to generate a plurality of segments which names segmentpiecewise pairs linear category clustering; and using a distance, anangle and a slope to perform an affinity group on the variable sequencesto generate a plurality of groups which names variables categoryaffinity grouping having a logically identity, wherein the variableslope in each segment is performed the affinity group with the segmentshaving the variable sequences which have logically identity or similarsimilarity in the segments to generate the groups.

The step of using the distance, the angle and the slope to perform theaffinity group on the variable sequences to generate the plurality ofgroups includes steps of: using a fuzzy C-means algorithm to cluster thevariable sequences by the distance to generate a first variablecategory; using a fuzzy spectral angle matching algorithm to cluster thevariable sequences by the angle to generate a second variable category;and using a fuzzy slope time series algorithm to cluster the variablesequences by the slope to generate a third variable category; anddetermining whether the first variable category, the second variablecategory and the third variable category have the same affinity group;wherein when two or more of the first variable category, the secondvariable category and the third variable category have the same affinitygroup, the variable categories having the same affinity group are thosehaving the logical identity.

The step of generating the category clustering data via the CDMAstructure processing the dataset categories according to the datasetcategories includes steps of: coding the dataset categories to generatea pseudo code and a carrier; outputting a carrier aggregation by anoutputting data according to the variable slope, the pseudo code and thecarrier; generating a channel module according to the segments and thegroups; calculating an error rate of all of the segments in the channelmodule; receiving the carrier aggregation; and decoding the carrieraggregation to revert to an actual value of this month.

The step of coding the dataset categories to generate the pseudo codeand the carrier includes steps of: generating the pseudo code accordingto a ratio generated from dividing an actual maximum value of each ofthe variable sequences in each of the segments by a maximum value oftwelve month moving average values of the variable sequence in thesegment, where the pseudo code is referred to as a weight; andgenerating a carrier of the twelve month moving average values of eachof the variable sequences and a carrier of a sine wave of each of thevariable sequences, wherein the variable sequences include a firstvariable sequence (China's GDP), a second variable sequence (China'sexport value), a third variable sequence (China's import value), afourth variable sequence (Exported to China from Taiwan), a fifthvariable sequence (Exported to China from Hong Kong), a sixth variablesequence (Exported to China from Korea), a seventh variable sequence(Exported to China from Vietnam), and eighth variable sequence (Shanghaicomposite index); wherein the carrier of the sine wave is generated bysubstituting eight sine wave parameter values into a sine wavegeneration formula, and mapped to the twelve month moving averagevalues, wherein the eight sine wave parameter values includes a maximumamplitude, a minimum amplitude, a skewness, a wave number, left skew andright skew, total points, a starting point and an ending point.

The step of outputting the carrier aggregation by the outputting dataaccording to the variable slope, the pseudo code and the carrierincludes steps of: generating a first spread spectrum according to thevariable slope of each of the variable sequences divided by the pseudocode in each of the segments; and selecting one of the carrier of thetwelve month moving average values of each of the variable sequences andthe carrier of the sine wave of each of the variable sequences tointegrate with the first spread spectrum to generate the carrieraggregation.

The category clustering data usually has a seasonal error, a fixed month(a periodical) effect (e.g. Lunar effect), a horizontal movement (e.g.Shift level), or a Turmoil interference, etc. Some data need to beexecuted by a Big Data or a Cloud computing service, so that a terminalhost arranges a group number of a compound variable channel combinationvia a channel module of the CDMA structure.

The step of generating the channel module according to the segments andthe groups includes steps of: arranging at least one compound variablechannel combination and a group number according to the groups and adependent variable; arranging a segment number of the segments accordingto the segments; and combining the group number and the segment numberto generate a true code; wherein the true code is a data code of thecompound variable channel combination and the segments; wherein thechannel module includes the at least one compound variable channelcombination.

The step of calculating the error rate of all of the segments in thechannel module includes step of: using a fuzzy C-means algorithm tocalculate a percentage of non similar attribute subsets of the categoryclustering in the compound variable channel combination to obtain theerror rate, wherein category divides similar objects to a lot of subsetshaving different group, so that the objects in the same subset havesimilar attributes. A correct rate is a percentage of the objects ofeach subset having the similar attributes over all the objects in thesubsets.

The step of receiving the carrier aggregation includes steps of:demodulating the carrier aggregation to obtain a second spread spectrum;and obtaining the variable slope by multiply the second spread spectrumby the pseudo code.

The step of decoding the carrier aggregation to revert to the actualvalue of this month includes steps of: reverting the variable slope tothe actual value of this month; and accumulating one by one the actualvalue of this month and an actual value of last month to obtain a curveof the twelve month moving average values; wherein when obtaining thecurve, the curve is stored as an historical data; wherein some specialcategory clustering data (e.g. seasonal data, cycle periodical data,etc.) are transmitted to a backstage host to search analysis rulesaccording to a historical record if necessary.

For further understanding of the instant disclosure, reference is madeto the following detailed description illustrating the embodiments ofthe instant disclosure. The description is only for illustrating theinstant disclosure, not for limiting the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1a shows a diagram of generating a channel module in accordancewith one embodiment of the present invention.

FIG. 1b shows a diagram of a data signal type in an outputting data anda receiving data in accordance with one embodiment of the presentinvention.

FIG. 1c shows a diagram of a carrier of twelve month moving averagevalues in accordance with one embodiment of the present invention.

FIG. 1d shows a diagram of a carrier of a sine wave in accordance withone embodiment of the present invention.

FIG. 1e shows a diagram of a slope of a Shanghai composite index inaccordance with one embodiment of the present invention.

FIG. 1f shows a diagram of a carrier aggregation of twelve month movingaverage values in accordance with one embodiment of the presentinvention.

FIG. 1g shows a diagram of a carrier aggregation of a sine wave inaccordance with one embodiment of the present invention.

FIG. 1h shows a diagram of an error rate in accordance with oneembodiment of the present invention.

FIG. 2a shows a diagram of dividing data variables in accordance withone embodiment of the present invention.

FIG. 2b shows a diagram of a code division multiple access (CDMA)structure in accordance with one embodiment of the present invention.

FIG. 2c shows a diagram of eight parameter values for generating a sinewave in a code division multiple access (CDMA) structure in accordancewith one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An embodiment of the present invention provides a data transmissionstructure based method for generating a category clustering data, inwhich the category clustering data is divided into a same groupaccording to a similarity of each other. The embodiment of the instantdisclosure provides a method for generating a category clustering datavia a code division multiple access (CDMA) structure including steps of:normalizing each of a plurality of variable sequences in a dataset,where the dataset includes the plurality of variable sequences, and thevariable sequences includes predictor variables and dependent variables,in which the predictor variables and dependent variables are continuousvalues on timeline; calculating twelve month moving average values ofeach of the variable sequences, namely the previous six values and thefollowing five values for each value in each variable sequence arecaptured, and the twelve values are executed a self-convolution toobtain the twelve month moving average values of each of the variablesequences; dividing the dataset to generate dataset categories, wheredividing the dataset is that the dataset is divided to obtain thedataset categories; and generating the category clustering data via theCDMA structure processing the dataset categories according to thedataset categories. The step of dividing the dataset includes steps of:using a variable slope of each of the variable sequences to perform asegment division on a corresponding variable sequence to generate aplurality of segments, namely that the variable sequences are divided toobtain a plurality of segments, and the segments are segment piecewisepairs linear category clustering; and using a distance, an angle and aslope to perform an affinity group on the variable sequences to generatea plurality of groups, namely that the variable slope in each segment isperformed the affinity group with the segments having the variablesequences which have logical identity or similar similarity in thesegments to generate the best groups, such as the variable sequences areclustered in each segment to obtain several groups, and groups arevariables category affinity grouping.

The step of generating the category clustering data via the CDMAstructure processing the dataset categories according to the datasetcategories includes steps of: coding the dataset categories to generatea pseudo code and a carrier; outputting a carrier aggregation by anoutputting data according to the variable slope, the pseudo code and thecarrier; generating a channel module according to the segments and thegroups; calculating an error rate of all of the segments in the channelmodule; receiving the carrier aggregation; and decoding the carrieraggregation to revert to an actual value of this month, so as to analyzethe category clustering data.

The step of coding the dataset categories to generate the pseudo codeand the carrier includes steps of: generating the pseudo code accordingto a ratio generated from dividing an actual maximum value of each ofthe variable sequences in each of the segments by a maximum value oftwelve month moving average values of the variable sequence in thesegment, where the pseudo code is referred to as a weight; andgenerating a carrier of the twelve month moving average values of eachof the variable sequences and a carrier of a sine wave of each of thevariable sequences, where the variable sequences include a firstvariable sequence (China's GDP), a second variable sequence (China'sexport value), a third variable sequence (China's import value), afourth variable sequence (Exported to China from Taiwan), a fifthvariable sequence (Exported to China from Hong Kong), a sixth variablesequence (Exported to China from Korea), a seventh variable sequence(Exported to China from Vietnam), and an eighth variable sequence(Shanghai composite index). The carrier includes the carrier of thetwelve month moving average values (MA12) and the carrier of the sinewave. The carrier of the sine wave is mapped to the twelve month movingaverage values, and is generated by bringing the eight sine waveparameter values including a maximum amplitude, a minimum amplitude, askewness, a wave number, left skew and right skew, total points, astarting point and an ending point into a sine wave generation formula.

The step of outputting the carrier aggregation by the outputting dataaccording to the variable slope, the pseudo code and the carrierincludes steps of: generating a first spread spectrum according to thevariable slope of each of the variable sequences divided by the pseudocode in each of the segments; and selecting one of the carrier of thetwelve month moving average values of each of the variable sequences andthe carrier of the sine wave of each of the variable sequences tointegrate with the first spread spectrum to generate the carrieraggregation.

The step of generating the channel module according to the segments andthe groups includes steps of: arranging at least one compound variablechannel combination and a group number according to the groups and adependent variable; arranging a segment number of the segments accordingto the segments; and combining the group number and the segment numberto generate a true code, where the true code is a data code of thecompound variable channel combination and the segments, so that the dataof the compound variable channel combination is transmitted affectivelyin the same channel. The channel module includes the at least onecompound variable channel combination.

The step of calculating the error rate of all of the segments in thechannel module includes a step of: using a fuzzy C-means algorithm tocalculate a percentage of non similar attribute subsets of the categoryclustering in the compound variable channel combination to obtain theerror rate, where the category is a method to cluster similar objects toa lot of subsets having different combination via a static algorithm, sothat the objects in the same subset have similar attributes. A correctrate means a percentage of clustering correctly the objects in thesubsets. The error rate equals to one subtracted by the correct rate.The correct rate is a percentage of the objects of each subset havingthe similar attributes over all the objects in the subsets.

The step of receiving the carrier aggregation includes steps of:demodulating the carrier aggregation to obtain a second spread spectrum;and obtaining the variable slope by multiply the second spread spectrumby the pseudo code.

The step of decoding the carrier aggregation to revert to the actualvalue of this month includes steps of: reverting the variable slope tothe actual value of this month; and accumulating one by one the actualvalue of this month and an actual value of last month to obtain a curveof the twelve month moving average values.

Referring to FIG. 2a , it is a diagram of dividing data variables inaccordance with one embodiment of the present invention. The step ofdividing the dataset uses a variable slope of each of the variablesequences to perform a segment division on a corresponding variablesequence to generate a plurality of segments which is referred to assegment piecewise pairs linear category clustering 31, and uses adistance, an angle and a slope to perform an affinity group on thevariable sequences to generate a plurality of groups which is referredto as variables category affinity grouping 21 having a logicallyidentity, so as to obtain the best groups number of clustering thedataset. The step of using the distance, the angle and the slope toperform an affinity group on the variable sequences to generate aplurality of groups having the logically identity includes using a fuzzyC-means algorithm 22 to cluster the variable sequences by the distanceto generate a first variable category, using a fuzzy spectral anglematching algorithm 26 to cluster the variable sequences by the angle togenerate a second variable category, and using a fuzzy slope time seriesalgorithm 24 to cluster the variable sequences by the slope to generatea third variable category, and determining whether the first variablecategory, the second variable category and the third variable categoryhave the same affinity group. When two or more of the first variablecategory, the second variable category and the third variable categoryhave the same affinity group, the variable categories having the sameaffinity group are those having the logically identity, namely thevariable slope in each segment is performed the affinity group with thesegments having the variable sequences which have logically identity orsimilar similarity in the segments to generate the best groups.

Referring to FIG. 2b , it is a diagram of a code division multipleaccess (CDMA) structure in accordance with one embodiment of the presentinvention. The CDMA structure 40 has one or more hardware to execute thestep of generating the category clustering data via the CDMA structure40 processing the dataset categories according to the datasetcategories, which includes: coding 52, outputting data 53, generating achannel module 54, calculating an error rate 55, receiving data 56, anddecoding 57, where the coding 52 codes the dataset categories togenerate the pseudo code and the carrier, the outputting data 53 outputsthe carrier aggregation according to the variable slope, the pseudo codeand the carrier, the generating the channel module 54 generates thechannel module according to the segments and the groups, the calculatingthe error rate 55 calculates the error rate of all of the segments inthe channel module, the receiving data 56 receives the carrieraggregation, and the decoding 57 decodes the carrier aggregation torevert to the actual value of this month.

The coding 52 includes: generating a pseudo code 60 and generating acarrier 61. The generating the pseudo code 60 generates the pseudo codeaccording to a ratio generated from dividing an actual maximum value ofeach of the variable sequences in each of the segments by a maximumvalue of twelve month moving average values of the variable sequence inthe segment, where the pseudo code is referred to as a weight, thevariable sequences include a first variable sequence (China's GDP), asecond variable sequence (China's export value), a third variablesequence (China's import value), a fourth variable sequence (Exported toChina from Taiwan), a fifth variable sequence (Exported to China fromHong Kong), a sixth variable sequence (Exported to China from Korea), aseventh variable sequence (Exported to China from Vietnam), and eighthvariable sequence (Shanghai composite index). The generating a carrier61 includes the: generating a carrier of the twelve month moving averagevalues 62 and generating a carrier of a sine wave 70, where thegenerating a carrier of a sine wave 70 generates the sine wave bysubstituting eight sine wave parameter values into a sine wavegeneration formula, in which the eight sine wave parameter valuesincludes a maximum amplitude 71, a minimum amplitude 72, a skewness 73,a wave number 74, left skew and right skew 75, total points 76, astarting point 77 and an ending point 78.

The outputting data 53 outputs the carrier aggregation by the outputtingdata according to the variable slope, the pseudo code and the carrier.Because after a curve of the twelve month moving average values isflattened, it needs to adjust a slope to apply to the carrier of thesine wave or the carrier of the twelve month moving average values, soas to execute the carrier aggregation. The outputting data 53 includes:spreading spectrum 63 and modulating 64. The spreading spectrum 63generates a first spread spectrum according to the variable slope ofeach of the variable sequences divided by the pseudo code in each of thesegments. The modulating 64 selects one of the carrier of the twelvemonth moving average values and the carrier of the sine wave tointegrate with the first spread spectrum to generate the carrieraggregation, where the carrier aggregation is referred to as a frequencyamplitude modulation.

The generating the channel module 54 includes: arranging at least onecompound variable channel combination 65 and generating a true code 66.The arranging the at least one compound variable channel combination 65arranges the at least one compound variable channel combination and agroup number according to the groups and a dependent variable to groupthe best compound variables having the logically identify by severalvariable sequences to generate the compound variable channelcombination, and arranges a segment number of the segments according tothe segments, where the dependent variable is a predicted variable andis the Shanghai composite index herein. The generating the true code 66combines the group number and the segment number to generate a true codeas a data code transmitted by CDMA structure. The first two number ofthe true code belong to the compound variable channel combination, andthe last four number of the true code belong to the segment number. Thetrue code is a data code of the compound variable channel combinationand the segments, so that the data of the compound variable channelcombination is transmitted affectively on the same channel. The channelmodule includes the at least one compound variable channel combination.

The calculating the error rate 55 calculates the error rate of all ofthe segments in the channel module by using a fuzzy C-means algorithm tocalculate a percentage of non similar attribute subsets of the categoryclustering in the compound variable channel combination to obtain theerror rate, and evaluating the error rate, where the error is fewer andthe correct rate is higher.

The receiving data 56 includes: demodulating 67 and dispreading spectrum68. The demodulating 67 demodulates the carrier aggregation to obtain asecond spread spectrum. The dispreading spectrum 68 obtains the variableslope by multiply the second spread spectrum by the pseudo code.

The decoding 57 includes: reverting to the actual value of this month69, that the variable slope is reverted to the actual value of thismonth and accumulated one by one by an actual value of last month toobtain a curve of the twelve month moving average values.

Referring to FIG. 2c , it is a diagram of eight parameter values forgenerating a sine wave in a CDMA structure in accordance with oneembodiment of the present invention. The generating the carrier of thesine wave 70 in the generating the carrier 61 substitutes eight sinewave parameter values into a sine wave generation formula, and mapped tothe twelve month moving average values, wherein the eight sine waveparameter values includes a maximum amplitude 71, a minimum amplitude72, a skewness 73, a wave number 74, left skew and right skew 75, totalpoints 76, a starting point 77 and an ending point 78. For example, themaximum amplitude 71 equals to 1, the minimum amplitude 72 equals to 0,a skewness 73 equals to 2, a wave number 74 equals to ½, left skew andright skew 75 are that an upper part of the sine wave is toward leftdeviation and a lower part of the sine wave is toward right deviation,total points 76 equals to 24, a starting point 77 equals to 1 and anending point 78 equals to 24.

The descriptions illustrated supra set forth simply the preferredembodiments of the instant disclosure; however, the characteristics ofthe instant disclosure are by no means restricted thereto. All changes,alterations, or modifications conveniently considered by those skilledin the art are deemed to be encompassed within the scope of the instantdisclosure delineated by the following claims.

What is claimed is:
 1. A method for generating a category clusteringdata via a code division multiple access (CDMA) structure, comprising:dividing a dataset to generate dataset categories, wherein the datasetincludes a plurality of variable sequences; and according to the divideddataset categories and, by processing the dataset categories via theCDMA structure, to generate the category clustering data; whereindividing the dataset includes: using a variable slope of each of thevariable sequences to perform a segment division on a correspondingvariable sequence to generate a plurality of segments which namessegment piecewise pairs linear category clustering; and using adistance, an angle and a slope to perform an affinity group on thevariable sequences to generate a plurality of groups which namesvariables category affinity grouping having a logically identity,wherein the variable slope in each segment is performed the affinitygroup with the segments having the variable sequences which havelogically identity or similar similarity in the segments to generate thegroups.
 2. The method according to claim 1, wherein using the distance,the angle and the slope to perform the affinity group on the variablesequences to generate the plurality of groups includes: using a fuzzyC-means algorithm to cluster the variable sequences by the distance togenerate a first variable category; using a fuzzy spectral anglematching algorithm to cluster the variable sequences by the angle togenerate a second variable category; and using a fuzzy slope time seriesalgorithm to cluster the variable sequences by the slope to generate athird variable category; determining whether the first variablecategory, the second variable category and the third variable categoryhave the same affinity group; wherein when two or more of the firstvariable category, the second variable category and the third variablecategory have the same affinity group, the variable categories havingthe same affinity group are those having the logical identity.
 3. Themethod according to claim 1, wherein generating the category clusteringdata via the CDMA structure processing the dataset categories accordingto the dataset categories includes: coding the dataset categories togenerate a pseudo code and a carrier; outputting a carrier aggregationby an outputting data according to the variable slope, the pseudo codeand the carrier; generating a channel module according to the segmentsand the groups; and calculating an error rate of all of the segments inthe channel module.
 4. The method according to claim 3, wherein codingthe dataset categories to generate the pseudo code and the carrierincludes: generating the pseudo code according to a ratio generated fromdividing an actual maximum value of each of the variable sequences ineach of the segments by a maximum value of twelve month moving averagevalues of the variable sequence in the segment; and generating a carrierof the twelve month moving average values of each of the variablesequences and a carrier of a sine wave of each of the variablesequences.
 5. The method according to claim 4, wherein outputting thecarrier aggregation by the outputting data according to the variableslope, the pseudo code and the carrier includes: generating a firstspread spectrum according to the variable slope of each of the variablesequences divided by the pseudo code in each of the segments; andselecting one of the carrier of the twelve month moving average valuesof each of the variable sequences and the carrier of the sine wave ofeach of the variable sequences to integrate with the first spreadspectrum to generate the carrier aggregation.
 6. The method according toclaim 5, wherein generating the channel module according to the segmentsand the groups includes: arranging at least one compound variablechannel combination and a group number according to the groups and adependent variable; arranging a segment number of the segments accordingto the segments; and combining the group number and the segment numberto generate a true code; wherein the true code is a data code of thecompound variable channel combination and the segments; wherein thechannel module includes the at least one compound variable channelcombination.
 7. The method according to claim 6, wherein calculating theerror rate of all of the segments in the channel module includes: usinga fuzzy C-means algorithm to calculate a percentage of non similarattribute subsets of the category clustering in the compound variablechannel combination to obtain the error rate.
 8. The method according toclaim 7, wherein generating the category clustering data via the CDMAstructure processing the dataset categories according to the datasetcategories further includes: receiving the carrier aggregation; anddecoding the carrier aggregation to revert to an actual value of thismonth.
 9. The method according to claim 8, wherein receiving the carrieraggregation includes: demodulating the carrier aggregation to obtain asecond spread spectrum; and obtaining the variable slope by multiply thesecond spread spectrum by the pseudo code.
 10. The method according toclaim 9, wherein decoding the carrier aggregation to revert to theactual value of this month includes: reverting the variable slope to theactual value of this month; and accumulating one by one the actual valueof this month and an actual value of last month to obtain a curve of thetwelve month moving average values.
 11. The method according to claim 4,wherein the carrier of the sine wave is generated by bringing eight sinewave parameter values into a sine wave generation formula, wherein theeight sine wave parameter values includes a maximum amplitude, a minimumamplitude, a skewness, a wave number, left skew and right skew, totalpoints, a starting point and an ending point.